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| Section | Weight | Objectives |
|---|---|---|
| Machine Learning | 13% | - Supervised, Unsupervised, and Reinforcement Learning - Algorithms and models (e.g., NLP, Computer Vision) - Deep Learning and Neural Networks |
| AI Fundamentals | 16% | - Types of AI and Machine Learning - Concepts and terminology of Artificial Intelligence - AI capabilities and limitations |
| CPMAI Methodology | 41% | - Phase III: Algorithm Selection & Model Training
|
| Data for AI | 13% | - Data preparation and preprocessing - Data strategy and governance - DataOps concepts |
| Managing AI | 8% | - Stakeholder management - Risk management in AI projects - Managing AI project teams and resources |
| Trustworthy AI | 9% | - Ethical considerations and bias - Transparency and explainability - Privacy and security |
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NEW QUESTION # 156
Your team was given a large dataset and has been tasked with organizing the data by type to make better insights from the results. You are facing problems with the approach that the previous project lead used which was a regression algorithm.
What type of algorithm is the best approach for this project?
Answer: A
NEW QUESTION # 157
An organization's leadership team is concerned about the ethical implications of operationalizing their AI model. How should the project manager address these concerns in their presentation to the team?
Answer: D
Explanation:
Demonstrating the use of bias detection tools directly addresses ethical concerns by showing how the project team evaluates fairness, identifies potentially discriminatory outcomes, and supports responsible operationalization of the AI model.
NEW QUESTION # 158
A project manager is reviewing the performance of an AI model used for predictive analytics in sales. The model's accuracy is within acceptable limits; however, its precision is low.
What is the cause for the precision issue?
Answer: B
NEW QUESTION # 159
Your team has created a model that is going to be used for monitoring systems and it needs to provide analysis on a weekly basis. What's the most appropriate Model Operationalization approach?
Answer: C
Explanation:
Batch prediction suits scenarios needing periodic analysis, like weekly monitoring, by processing accumulated data in scheduled batches.
NEW QUESTION # 160
Eight months after deployment, a credit risk model's performance has gradually degraded. The input data pipeline is functioning correctly, but economic conditions have changed significantly since training.
What is the most likely cause?
Answer: C
Explanation:
Concept drift occurs when the statistical relationship between input features and the target variable changes over time. Because the model encodes historical relationships, shifting economic conditions invalidate its learned patterns and require retraining on current data.
NEW QUESTION # 161
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